The Oven Scheduling Problem (OSP) is a complex combinatorial optimization task characterized by parallel batching and heterogeneous ovens operating under tight production constraints. Existing methods, such as constraint programming and local search, are effective but often limited by computational cost or restricted search diversity. This paper introduces CP-MEME, a hybrid (1+1)-Evolutionary Framework that combines the constraint-based feasibility reasoning of constraint programming with the adaptive exploration capabilities of evolutionary computation. In the initialization phase, a CP-SAT model rapidly produces a feasible and near-optimal baseline schedule. The subsequent phase employs a single-individual (1+1)-Evolution Strategy enhanced by adaptive local search to intensify promising solutions while preserving feasibility. When search stagnation is detected, a penalty-guided perturbation mechanism executes batch swaps to diversify the search trajectory and escape local optima. Experimental evaluations on benchmark OSP datasets demonstrate that CP-MEME outperforms exact and state-of-the-art local search methods, achieving consistent improvements in both solution quality and computational robustness.